Currently Viewing:
The American Journal of Managed Care March 2017
Currently Reading
Impact of a Pharmacy-Based Transitional Care Program on Hospital Readmissions
Weiyi Ni, PhD; Danielle Colayco, PharmD, MS; Jonathan Hashimoto, PharmD; Kevin Komoto, PharmD, MBA; Chandrakala Gowda, MD, MBA; Bruce Wearda, RPh; and Jeffrey McCombs, PhD
Private Sector Accountable Care Organization Development: A Qualitative Study
Ann Scheck McAlearney, ScD; Brian Hilligoss, PhD; and Paula H. Song, PhD
Scaling Lean in Primary Care: Impacts on System Performance
Dorothy Y. Hung, PhD; Michael I. Harrison, PhD; Meghan C. Martinez, MPH; and Harold S. Luft, PhD
Patient Experience Midway Through a Large Primary Care Practice Transformation Initiative
Kaylyn E. Swankoski, MA; Deborah N. Peikes, PhD, MPA; Stacy B. Dale, MPA; Nancy A. Clusen, MS; Nikkilyn Morrison, MPPA; John J. Holland, BS; Timothy J. Day, MSPH; and Randall S. Brown, PhD
A Better Way: Leveraging a Proven and Utilized System for Improving Current Medication Reconciliation Processes
Ajit A. Dhavle, PharmD, MBA; Seth Joseph, MBA; Yuze Yang, PharmD; Chris DiBlasi, MBA; and Ken Whittemore, RPh, MBA
Effects of an Enhanced Primary Care Program on Diabetes Outcomes
Sarah L. Goff, MD; Lorna Murphy, MA, MPH; Alexander B. Knee, MS; Haley Guhn-Knight, BA; Audrey Guhn, MD; and Peter K. Lindenauer, MD, MSc
Consumer-Directed Health Plans: Do Doctors and Nurses Buy In?
Lucinda B. Leung, MD, MPH, and José J. Escarce, MD, PhD
Improvements in Access and Care Through the Affordable Care Act
Julie A. Schmittdiel, PhD; Jennifer C. Barrow, MSPH; Deanne Wiley, BA; Lin Ma, MS; Danny Sam, MD; Christopher V. Chau, MPH; Susan M. Shetterly, MS
Does Paid Versus Unpaid Supplementary Caregiving Matter in Preventable Readmissions?
Hsueh-Fen Chen, PhD; Taiye Oluyomi Popoola, MBBS, MPH; and Sumihiro Suzuki, PhD

Impact of a Pharmacy-Based Transitional Care Program on Hospital Readmissions

Weiyi Ni, PhD; Danielle Colayco, PharmD, MS; Jonathan Hashimoto, PharmD; Kevin Komoto, PharmD, MBA; Chandrakala Gowda, MD, MBA; Bruce Wearda, RPh; and Jeffrey McCombs, PhD
Patients receiving postdischarge care from pharmacists had a 28% lower risk of readmission at 30 days and a 31.9% lower risk at 180 days compared with usual care.
ABSTRACT

Objectives: 
Avoidable readmissions of patients discharged from hospitals are a major concern. This study evaluates the impact of pharmacist-provided postdischarge services on hospital readmissions for members of a US managed Medicaid health plan.

Study Design: Prospective cohort study.

Methods: Synergy Pharmacy Solutions (SPS) initiated a transition of care (TOC) service for high-risk members of the Kern Health Systems (KHS) managed Medicaid plan. Over 1100 patients were referred to SPS between April 2013 and March 2015. KHS classified hospitalized members as high risk for readmission based on prior healthcare utilization, a health risk assessment questionnaire, and the use of the Johns Hopkins predictive modeler. This study compares SPS TOC recipients with a matched sample of KHS members discharged from nonintervention hospitals. Thirty-day and 180-day readmissions and time-to-readmission were defined as outcomes. Logistic regression and Cox model were estimated, controlling for demographics, diagnostic and drug profiles, and prior hospital utilization.

Results: KHS identified 1763 high-risk discharges from nonintervention hospitals, of which 1005 and 669 were matched to 830 and 558 selected SPS patients in 30-day and 180-day populations, respectively. The SPS postdischarge intervention reduced the risk of readmission within 30 days by 28% (odds ratio [OR], 0.720; 95% confidence interval [CI], 0.526-0.985) and within 180 days by 31.9% (OR, 0.681; 95% CI, 0.507-0.914). The estimated effect of the SPS intervention from the Cox model was a reduction in risk of 25% (hazard ratio, 0.749; 95% CI, 0.566-0.992).

Conclusions: A community pharmacy-based postdischarge TOC program can significantly reduce readmission rates at 30 and 180 days compared with usual discharge care.

Am J Manag Care. 2017;23(3):170-176
Takeaway Points

An ambulatory care pharmacy-based transition of care program reduced 30-day and 180-day readmission rates by 28% and 31.9%, respectively, compared with usual discharge care. 
  • The pharmacist interventions focused on patient education, resolving medication-related problems, and facilitating access to postdischarge appointments and medications. 
  • Previous studies evaluating pharmacist-provided care transitions have focused on specific disease states, had shorter follow-up periods, and/or included only academic or integrated health systems. The standalone clinic evaluated herein may be more generalizable to a variety of different practice settings. 
  • Future research may identify specific risk factors and interventions that affect readmissions.
Patients with complex medical histories and medication regimens who are admitted to the hospital are at risk for readmission due to a number of factors, including lapses in the continuity of care. The average 30-day readmission rate in the United States is about 16%.1 These high readmission rates have imposed a significant clinical and economic burden on the US healthcare system. As a result, the Hospital Readmissions Reduction Program, enacted in October 2012, directed that CMS reduce payments to hospitals with excess 30-day readmission rates for conditions like acute myocardial infarction (AMI), heart failure, pneumonia, chronic obstructive pulmonary disease (COPD), total hip arthroplasty, and total knee arthroplasty.2 Thus, it is critical to identify the reasons for readmissions and to implement programs to decrease the risk of readmissions. 

Suboptimal medication therapy during the transition of care (TOC) period following hospital discharge is a major contributory factor to hospital readmissions and increased healthcare utilization.3 Forster et al estimated that 11% of patients experienced an adverse drug event after discharge from inpatient services, and 27% of readmissions were considered to be preventable if the patient had received appropriate postdischarge medication monitoring.4 Additionally, several services have been shown to impact hospital readmissions, including patient education, medication adherence counseling, and medication reconciliation.5-11 Pharmacist involvement in discharge counseling, medication reconciliation, and telephone follow-up has resulted in a lower incidence of preventable adverse drug events.7-10 Consequently, it is reasonable to expect that pharmacist-led TOC services may decrease readmission rates.

Synergy Pharmacy Solutions (SPS) in Bakersfield, California, initiated an ambulatory care pharmacy-based TOC service in 2013 for recently discharged members of the Kern Health Systems (KHS) managed Medicaid health plan who were classified as high risk based on their healthcare utilization, medical history, and social history and the use of the Johns Hopkins predictive modeler. High-risk members admitted to a single local hospital were referred to SPS for postdischarge services. Over 1100 members were referred to SPS between April 2013 and March 2015. 

This study evaluated the effect of the ambulatory care pharmacy-based TOC services provided by SPS on 30-day and 180-day readmissions compared with a control group of matched KHS members discharged from neighboring hospitals.

METHODS

Ambulatory Care Pharmacy-Based TOC Program


The risk of readmission for individuals enrolled in KHS's managed Medicaid plan was evaluated based on their history of hospitalizations, prescription medication utilization, and social history. Adult patients discharged from a local hospital who were at high risk for readmission were automatically referred to the SPS TOC program. Those who met the following criteria were then excluded by the SPS team: discharged to a skilled nursing facility, rehabilitation facility, or hospice; died in hospital; left hospital against medical advice; or hospitalized for an elective procedure, obstetrical complications, substance abuse, a urinary tract infection, or a suicide attempt.

Intervention

Once the qualified patient agreed to participate in the SPS TOC program, medications were reconciled and any discrepancies between the patient’s self-reported medication use and the hospital discharge orders were noted. Over the 30 days following discharge, the pharmacists worked with the outpatient providers to resolve any medication-related problems, such as inappropriate therapy, therapeutic duplications, and potential drug interactions. In addition, the pharmacists counseled patients to improve medication adherence. The pharmacy staff reinforced the discharge care plan, including postdischarge appointments, facilitating authorizations for specialist care, arranging transportation for appointments, and working with each patient’s dispensing pharmacy to resolve insurance-related issues. Patients requiring additional assistance were invited for a face-to-face visit, which included more intensive counseling and assistance with organizing medications. 

Medication management services were documented directly into the existing electronic health record (EHR) system of the ambulatory care pharmacy, which included customized reporting capabilities. Daily reports were generated by the pharmacy team, and follow-up tasks were assigned accordingly. The clinical pharmacy team acted as a liaison to bridge the communication gaps between the patients, their prescribers, and their dispensing pharmacy, thereby facilitating improvements in the continuity of care between the inpatient and outpatient settings. 

Data Collection

The primary source of data for this study was the KHS paid claims database, which covered all enrolled beneficiaries’ inpatient records, outpatient services, emergency department visits, and prescription claims for services rendered within the United States. International Classification of Diseases, Ninth Revision, Clinical Modification codes were used to identify diagnoses. Medications were identified using specific therapeutic class codes. The data related to TOC services were collected from the EHR system at SPS. 

Study Population

The intervention and control patient populations were selected from the pool of adult Medicaid managed care members of KHS's health plan who were discharged from either the study hospital or the control hospital (other neighboring hospitals contracted with KHS in Kern County, California) and met the following inclusion criteria at the time of discharge: active members of KHS with an inpatient stay at participating hospitals and at least 1 of the following: 1) high risk, as determined by KHS’s algorithm, including prior healthcare utilization and social history; 2) discharged with prescription claims for 5 or more medications; 3) admitted to any local hospital within the last 45 days. 

For the intervention patients referred to SPS, an index date was defined as the discharge date of their hospitalization immediately preceding referral. All referred patients were screened for 6 months of continuous health plan enrollment prior to the index hospitalization and 30 days of postdischarge data. A second screen requiring 180 days of postdischarge data was applied for hospital admissions used in the analysis of readmission at 6 months.

The control group was identified retrospectively by applying the KHS risk-screening algorithm to its members discharged from neighboring hospitals between October 2012 and March 2015. In order to identify an index hospitalization for each control group patient, all hospitalizations for each patient were converted into episodes of hospital care and matched to the index episode in an intervention group member based on the number of prior hospitalizations and length of stay (LOS) (± 1 day). In order to maximize the power of the analysis, we included all the patients from the control group who were matched to at least 1 patient in the intervention group. Thus, intervention patients could have more than 1 matched control hospitalization in the final study population.

A total of 1123 patients were referred to SPS, of which 830 met the enrollment criteria for the 30-day analysis and were matched to 1005 patients receiving usual care. A total of 558 SPS patients met the enrollment criteria for the 180-day analysis and were matched to 669 usual care patients (Figure). In the intervention group, all patients referred to SPS were included in this intent-to-treat (ITT) analysis, including those who did not qualify for services, who could not be contacted, and who declined services. 

Outcome Measures

The primary outcome measures were all-cause 30-day and 180-day hospital readmissions, which were defined as inpatient stays within 30 or 180 days after the index hospitalization discharge date. The 180-day window was analyzed in order to evaluate the persistence of the intervention beyond 30 days. Time to readmission was also calculated and used as a secondary outcome measure. As a sensitivity analysis, the total count of hospital readmissions within 30 days and 180 days after the index date were compared between the intervention and control groups.

Statistical Analysis 

Descriptive statistics were applied to test for differences in demographics and clinical characteristics between the intervention and control groups. A χ2 test was used to test for baseline differences between intervention and control patients in the distribution of gender, race, age, and indicator of prior hospitalizations. Student’s t test was utilized to compare the mean index hospitalization LOS and number of medications.

Logistic regression was used to estimate the impact of the TOC intervention on the likelihood of a 30-day or 180-day readmission. These models controlled for age, gender, race, prior hospitalizations (yes/no), LOS of the index hospitalization, inpatient diagnoses prior to and including the index hospitalization, and the mix of medication classes used by the patient over the 6 months prior to admission. Time to readmission was analyzed using a Cox proportional hazards model, controlling for the same covariates used in the logistic analyses. The study population for the Cox analysis was the same population used in the 30-day readmission analysis. For the sensitivity analysis, ordinary least squares (OLS) regression and Poisson regression models were used to estimate the effects of the TOC intervention on the count of hospitalizations within 30 days and 180 days after the index hospitalization controlling for demographic information, prior healthcare utilization, and comorbidities. Data analyses were performed using SAS version 9.4 (SAS Institute Inc, Cary, North Carolina) and STATA version 12 (StataCorp, College Station, Texas). 

RESULTS

A total of 1123 patients were referred to the SPS TOC program during the study period, of whom 841 were continuously enrolled in the KHS Medicaid plan 6 months prior to and 30 days after the index hospitalization. After matching, 830 intervention patients and 1005 control observations were included in the logistic regression model for 30-day readmission. For the analysis of 180-day readmission, 564 referred patients were continuously enrolled in the KHS plan 6 months prior to and 30 days after the index hospitalization, of which 558 from the intervention group and 669 from the control group were matched. 

Table 1 compares the baseline characteristics of the intervention and control groups. In the 30-day readmission population, the age, gender, and number of medications from the intervention and control groups were not statistically different, whereas the intervention group tended to have a higher proportion of prior inpatient admissions and longer index hospitalization LOS. In the 180-day matched population, the age, race, and gender of the 2 groups were similar. As in the 30-day population, the intervention group had a higher proportion of patients with prior inpatient stays and longer LOS. In order to control for the differences between groups, these demographics and clinical characteristics were used as independent variables in both the logistic and Cox models. 

 
Copyright AJMC 2006-2017 Clinical Care Targeted Communications Group, LLC. All Rights Reserved.
x
Welcome the the new and improved AJMC.com, the premier managed market network. Tell us about yourself so that we can serve you better.
Sign Up
×

Sign In

Not a member? Sign up now!